Asthma and obesity are leading public health concerns that disproportionately affect low-income and minority children. Epidemiologic studies have consistently shown an association between asthma and obesity. Among all US populations, asthma prevalence is highest in Puerto Ricans and lowest in Mexican Americans. Obesity is high in both populations. Obese asthma may be a unique phenotype of asthma, with a more difficult clinical course and altered response to asthma controller therapy. Independent risk factors for both obesity and asthma disproportionately impact low income and minority children. Shared early-life exposures are known risk factors. Shared genetic risk factors are also important Twin studies indicated that 8% of the genetic component of obesity is shared with asthma. A comprehensive study of low income minority children will help to uncover the contributions of genetics, socio-demographic, and early-life events to the co-occurrence of asthma and obesity in this population. Latinos are an ideal population to tease apart the genetic, social and environmental risk factors associated with obesity and asthma. Latinos are the largest minority population in the United States and the largest demographic group among all U.S. children. Latinos are admixed with considerable variation in genetic ancestry, both at the individual and group levels, and this can be leveraged to untangle complex associations between asthma and obesity susceptibility. We performed the largest admixture mapping scan of asthma and obesity in the U.S. We identified three admixture mapping peaks that were associated with both asthma and obesity, suggesting there are pleiotropic risk variants in these regions that contribute to both asthma and obesity in Latino populations. The goal of this roposal is to identify novel genetic variants associated with both asthma and obesity by deep resequencing of candidate regions identified through admixture mapping. We will also test for correlations between the genetic variation and levels of gene expression to identify putative expression quantitative trait loci (eQTLs) that are associated with each condition. Finally, we will test identified eQTLs in several independent populations from the NHLBI sponsored EVE asthma consortium.